Samuel Kalman, Purdue University Undergraduate
Jonathan Bosch, Syracuse University Undergraduate
Basketball has recently been considered more of a “position-less” sport. Most NBA players have skills, styles, and tendencies that cannot be defined by a single traditional position. With NBA player data that accounts for a player’s efficiency, opportunity, and tendencies, we were able to implement unsupervised machine learning techniques to create a framework for how NBA playing styles can be clustered on the court and used for strategic decision making when building rosters and creating lineup rotations. These player clusters are considered new positions, and they give more accurate and detailed insight to what role a player possesses when on the court and how effective he could be in that role. Our unique methods give players a soft assignment for all clusters, that is, a probabilistic weighting onto each of the clusters indicating their likelihood of specific cluster fit. After analyzing the distribution of the various player stats within each new position, we were able to generate a player role for each cluster.
We present a more specific way to consider player types and positions in the NBA, while providing insight into which combination of player types yield the most effective basketball performance. Our models also contain a predictive component where we can predict the net rating of a potential lineup. As we have recently witnessed a massive change in playing style by most of the NBA, we offer a more accurate approach for analyzing and understand the roles, responsibilities, and combinations of specific groups of players in the NBA.